code: def map_param(self, name, parameter) -> None:
      for unmapped_prefix in self._unmapped_prefixes:
          if name.startswith(unmapped_prefix):
              inference_logger().debug(
                  f"Ignoring: {name} for {unmapped_prefix}"
              )
              return
  
      for transformer_prefix in self._transformer_prefixes:
          if name.startswith(transformer_prefix):
              popped_name = name[len(transformer_prefix) + 1 :]
              layer_idx = popped_name.split(".")[0]
              assert (
                  layer_idx.isdigit()
              ), f"expected name to start w. list index but got {layer_idx} instead, name={name}"
              layer_idx = int(layer_idx)
              inference_logger().debug(
                  f"Setting: {'.'.join(popped_name.split('.')[1:])} for layer-idx={layer_idx} to {parameter.shape}"
              )
              self._transformer_params[layer_idx].set_dependency(
                  ".".join(popped_name.split(".")[1:]), parameter
              )
              return
  
      try:
          inference_logger().debug(
              f"Setting: {name} to {parameter.shape}"
          )
          self._non_transformer_params.set_dependency(name, parameter)
      except ValueError:
          # Catch the ValueError here from the non_transformer_params because we are knowingly
          # calling it with something that may not match. This should allow us to raise a slightly more
          # informative error message.
          raise ValueError(
              f"Cannot find container for {name}, please double check the Containers/ContainerMap"
          )
  
imports: import json
  from abc import ABC, ABCMeta, abstractmethod
  from typing import Any, Iterable, List, Optional, Union
  import torch
  from ..config_v2 import RaggedInferenceEngineConfig
  from ..checkpoint import CheckpointEngineBase
  from ..logging import inference_logger
  from .layer_container_base import LayerContainer
  from .inference_model_base import DSInferenceModelBase
  from .flat_model_helpers import flatten_inference_model, make_param_filename, make_metadata_filename, ModelMetadata, restore_inference_model
label_desc: Use image node_id 7 for calling the ContainerMap obj's underlying member method code with example usage: obj.map_param(name, parameter) without return types